Information-Aware 2^n-Tree for Efficient Out-of-Core Indexing of Very Large Multidimensional Volumetric Data

We discuss a new efficient out-of-core multidimensional indexing structure, information-aware 2n-tree, for indexing very large multidimensional volumetric data. Building a series of (n-1)-Dimensional indexing structures on n-Dimensional data causes a scalability problem in the situation of continually growing resolution in every dimension. However, building a single n-Dimensional indexing structure can cause an indexing effectiveness problem compared to the former case. The information-aware 2n-tree is an effort to maximize the indexing structure efficiency by ensuring that the subdivision of space have as similar coherence as possible along each dimension. It is particularly useful when data distribution along each dimension constantly shows a different degree of coherence from each other dimension. Our preliminary results show that our new tree can achieve higher indexing structure efficiency than previous methods.